A growing number of cameras are being used for various monitoring and surveillance applications indoors and outdoors. Examples are monitoring networks for home and commercial properties, vehicle surveillance systems, airport securities, boarder patrolling, etc. Modern surveillance cameras are mostly stationary, although subject to unintentional jitters or displacements, and monitor the same scene over a long period of time. It would be beneficial to enable a monitoring system to automatically detect new activities in the scene. One way to achieve this is to model the background of the scene, such that objects that are not integral to the scene can be identified and segregated from the background. Accordingly, upon detecting anomalous objects moving to the scene, the system may raise an alarm of any sort or initiate further actions, such as fetching image data for object recognition analytics, recording and/or broadcasting video images to authorities, etc.
Most prior art on background modeling are based on pixel data processing. For example, a simple way to detect motion is to threshold collocated pixel difference in the successive video frames. If the difference is larger than a given threshold, motion is detected and an alert may be raised. This approach would not work in dynamic environments where the background of the monitored scene is not still. Examples of dynamic bodies are flowing water, waving trees, moving vegetation, and any other natural motions. Changing daylight conditions and flickering street lights are usually not of interest either. Traditional decision making using pixel values are sensitive to all these subtle variations. Furthermore, not all activities are genuine to trigger a security alarm. Further, as far as the economy of data handling is concerned, it would be costly to store and, more so, to transport unnecessary data.
A robust motion alarm should be resilient against false alarms, that include the above mentioned activities; but efficient in detecting salient activities such as moving people, animals or vehicles. Therefore, any technique that attempts to detect anomalous objects in the scene must be insensitive towards natural changes, but intelligently detect genuine moving objects over the background and handle useful data efficiently. The present invention utilizes an efficient background modeling technique to segregate a scene as foreground and background. The foreground areas are analyzed to detect new objects and verify genuine activities in the scene. Robustness of this approach is further achieved by rejecting false alarms. The background model may further be utilized in video compression application.